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• W2526967658 abstract "Classification models can demonstrate apparent prediction accuracy even when there is no underlying relationship between the predictors and the response. Variable selection procedures can lead to false positive variable selections and overestimation of true model performance. A simulation study was conducted using logistic regression with forward stepwise, best subsets, and LASSO variable selection methods with varying total sample sizes (20, 50, 100, 200) and numbers of random noise predictor variables (3, 5, 10, 15, 20, 50). Using our critical values can help reduce needless follow-up on variables having no true association with the outcome." @default.
• W2526967658 created "2016-10-07" @default.
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• W2526967658 date "2017-05-23" @default.
• W2526967658 modified "2023-09-26" @default.
• W2526967658 title "A simulation based method for assessing the statistical significance of logistic regression models after common variable selection procedures" @default.
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• W2526967658 doi "https://doi.org/10.1080/03610918.2016.1230216" @default.
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